x402-solar-mass
Solar Mass: Mass of solar.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Solar Mass: Mass of solar.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of explaining behavior, but it only states a noun phrase. It does not disclose return format, units, precision, or whether the result is a numeric constant or an object, so an agent cannot predict the call's output.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The text is short, but the structure is essentially a label followed by a tautological fragment 'Mass of solar.' Little is conveyed per word beyond what the name already says, so this is closer to under-specification than to effective conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema and no annotations, the description should explain what value will be returned and in what form. It does neither: an agent cannot tell whether it gets kilograms, solar mass units, or a JSON object. For a simple constant tool this is a small but real completeness gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema is empty, so there are no parameters to document and the schema coverage is effectively complete. Because this is a zero-parameter tool, the baseline for this dimension is high and the description need not add parameter-level meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Solar Mass: Mass of solar.' essentially restates the tool name ('solar-mass' becomes 'mass of solar') without a specific verb or function. It does not explain whether this tool returns a constant, performs a conversion, or provides metadata, and it does not distinguish itself from sibling constants like x402-earth-mass or x402-proton-mass.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool versus any alternative. The description provides no exclusions or context for choosing it over related mass/constant tools, leaving the agent to infer usage entirely from the name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The tool set is saturated with near-duplicates and synonyms: character-count vs char-count, clamp vs clamp-value, is-abundant vs is-abundant-num vs is-abundant-number, and fetch vs browser-scrape vs web-scrape vs text-scrape. Generic names like 'difference', 'normalize', 'range', and 'partition' make the boundaries even harder for an agent to determine.
Most tools share a x402- kebab-case prefix, but the set mixes noun-only names (math, hash, prime, time), verb-first names (get_stats, find, validate), auto-generated names (x402-publish-1787853294312-base-account), and inconsistent variants like temp vs temperature vs temperature-convert. This is not a coherent verb_noun convention despite the common prefix.
1677 tools is an extreme count that creates selection paralysis and makes coherent agent use impractical. A utility or marketplace server at this scale needs sub-services or namespacing rather than a flat tool list.
The surface has broad token coverage across many utility categories, but the marketplace aspect is incomplete: service_discovery and get_stats exist, yet there are no generic publish, update, delete, or account-management operations. Utility families also contain redundant variants without clear completion or lifecycle structure.